Biologically motivated space-variant filtering for robust optic flow processing

We describe and test a biologically motivated space-variant filtering method for decreasing the noise in optic flow fields. Our filter model adopts certain properties of a particular motion-sensitive area of the brain (area MT), which averages the incoming motion signals over receptive fields, the sizes of which increase with the distance from the center of the projection. We use heading estimation from optic flow as a criterion to evaluate the improvement of the filtered flow field. The tests are conducted on flow fields calculated with a standard flow algorithm from image sequences. We use two different sets of image sequences. The first set is recorded by a camera which is installed in a moving car. The second set is derived from a database containing three dimensional data and reflectance information from natural scenes. The latter set guarantees full control of the camera motion and ground truth about the flow field and the heading. We test the space-variant filtering method by comparing heading estimation results between space-variant filtered flow, flow filtered by averaging over domains of the visual field with constant size (constant filtering) and raw unfiltered flow. Because of noise and the aperture problem the heading estimates obtained from the raw flows are often unreliable. Estimated heading differs widely for different sub-sampled calculations. In contrast, the results obtained from the filtered flows are much less variable and therefore more consistent. Furthermore, we find a significant improvement of the results obtained from the space-variant filtered flow compared to the constant filtered flow. We suggest extensions to the space-variant filtering procedure that take other properties of motion representation in area MT into account.